In recent years the decoding of surface electromyography signals produced by skeletal muscles during the execution of hand gestures has found many applications in human–machine interface, myoelectric control, rehabilitation, and other domains. The availability and diversity of electromyography data has encouraged advancements in processing and data analysis algorithms that pave the way for the development of more complex applications. This also includes fusion with other data types for gesture recognition. This Special Issue aims to communicate novel ideas and new theoretical frameworks/algorithms that are close to real-life applications. It should provide the opportunity for machine learning, robotics, and AI researchers from academia and industry to present their latest work, share ideas, and strengthen contacts within this mixed “EMG/Data Processing” and “University/Industry” R&D community.
Topics of interest include (but are not limited to):
- Hand gesture recognition from sEMG sensor data
- sEMG and other sensors in gesture recognition
- Deep learning and gesture recognition
- Hand gesture recognition for gaming and virtual reality applications
- Hand gesture recognition for robotics applications
- Hand gesture recognition for medical and rehabilitation applications
- sEMG and IMU sensor large datasets
- sEMG in prosthesis control and grasping
- sEMG for biometrics applications (person verification and identification)
Special Issue Guest Editors
Prof. Dr. Athanassios Skodras
Prof. Dr. Jan Cornelis
Prof. Dr. Bart Jansen
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- surface electromyography
- machine learning
- hand gesture recognition
- deep learning
- time-series analysis and modeling